Predicting c-KIT Inhibitor Efficacy in Patient-Derived Models of Sinonasal Mucosal Melanomas through Integrated Histogram Analysis of Whole-Tumor DKI, IVIM, and DCE-MRI

To evaluate whole-tumor histogram analysis of diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) in predicting the efficacy of imatinib, a c-KIT inhibitor, for treating patient-derived models derived from sinonasal mucosal melanomas (MM).

This study included 38 patients with histologically confirmed sinonasal MM, who underwent DKI, IVIM, and DCE-MRI. Patient-derived tumor xenograft models and precision-cut tumor slices were established to evaluate tumor response to imatinib. Whole-tumor histogram analysis was conducted on imaging parameters, and logistic regression models were applied to determine the predictive value of these metrics in differentiating responders from nonresponders.

Among the 38 patients with sinonasal MM, 12 were classified as responders and 26 as nonresponders based on patient-derived tumor xenograft and precision-cut tumor slice model responses to imatinib. The DKI model revealed significant differences in mean, median, 10th percentile, and 90th percentile values of Dk and K between responders and nonresponders (P < 0.05). The IVIM model indicated significant differences in 10th percentile and mean values of D, with kurtosis f being a strong predictor. The DCE-MRI model, using the 90th percentile K^trans metric, demonstrated robust predictive performance, achieving an AUC of 0.89, with 80.77% specificity and 91.67% sensitivity. The combined logistic model integrating DKI, IVIM, and DCE-MRI metrics produced the highest predictive accuracy, with an AUC of 0.90.

Whole-tumor histogram analysis of DKI, IVIM, and DCE-MRI offers a noninvasive method for predicting the efficacy of c-KIT inhibitors in sinonasal MMs, presenting valuable implications for guiding targeted treatment in this rare cancer type.